This work investigates the ideal distribution of sensors in networked arrays. MATLAB models these arrays and simulates the results these networks obtain using active and passive sonar. These results determine the ideal sensor placement for optimal parameter detection and estimation of targets. This work's first part focuses on active sonar networks with a fixed number of sensors in a differing number of arrays. MATLAB simulates the data of these sensors taking into account the geometries and velocities of the arrays and targets, then estimates the parameters of the targets using an elliptical filter, a conventional beamformer, a matched filter and one of three fusion methods. This work compares the performance of each network and fusion method. This work shows that the adding more arrays, regardless of size, enhances the overall performance of the network. It also shows the larger arrays obtain more robust parameter estimation. The second part of this work investigates the effects of uncertainty of the array position and orientation using passive sonar. Two networks, one with 2 32-channel arrays and one with 8 2-channel arrays, estimate a sound source's location using a conventional beamformer. MATLAB simulates the data taking into account the geometries of the arrays and the sound source. The results of these simulations show that when uncertainty of position and orientation increases, the better the smaller arrays estimate the location of the sound source compared to the larger arrays. / Master of Science